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Knit directory: BITHub-preprocessing/
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This R markdown document contains the complete code for the prep-rocessing of bulk RNA-sequencing files for Brain Integrative Transcriptome Hub (BITHub). All datasets were retrieved as described in Table 1 in the GitHub Repo.
In order to rerun the scripts - make sure to change the directories
to the location of where the input files for pre-processing are stored.
All directories are defined in
code/preprocess/raw_file_locations.R. A copy of each
processed metadata will also be saved in ’output/metadata/` for plotting
purposes.
All other files are stored within the data folder of this R workflow directory.
library(recount3)
library(magrittr)
library(tibble)
library(reshape2)
library(SummarizedExperiment)
library(corrplot)
library(dplyr)
library(ggvenn)
library(pander)
library(gridExtra)
library(variancePartition)
library(DT)
library(EnsDb.Hsapiens.v86)
library(singscore)
library(AnnotationHub)
library(stargazer)
library(ggfortify)
library(glue)
library(cowplot)
library(broom)
library(glmpca)
library(DT)
library(naniar)
source("code/preprocess/functions.R")
source("code/preprocess/def_stages.R")
source("code/preprocess/raw_file_locations.R")
strc_acr <- map_df(structure_acronym, ~as.data.frame(.x), .id="id") %>%
set_colnames("StructureAcronym")
colnames(strc_acr)[2] = c("Structure")
rgns = map_df(regions, ~as.data.frame(.x), .id="id") %>%
set_colnames("Regions")
colnames(rgns)[2] = c("StructureAcronym")
rgns_ftl = map_df(regions_fetal, ~as.data.frame(.x), .id="id") %>%
set_colnames("Regions")
colnames(rgns_ftl)[2] = c("StructureAcronym")
bseq.annot = read.csv(here::here("data/annotations/BrainSeq-metadata-annot.csv"), header = TRUE)
load(file.path(bseq, "rse_gene_unfiltered.Rdata"), envir = .GlobalEnv)
load(file.path(bseq,"methprop_pd.Rdata"), envir = .GlobalEnv)
x = rse_gene@colData
x <- as.data.frame(x)
x <- as.data.frame(t(x))
replicated <- colnames(x)[grep(",", x["SAMPLE_ID",])]
y <- as.list(x)
y[replicated] <- lapply(y[replicated], function(z) {
# which variables to merge
to.weight <- which(sapply(z, length) > 1 & sapply(z, class) %in% c("numeric", "integer"))
# weighting of the merge
weighting <- z$numReads # total reads
weighting <- weighting / sum(weighting) # rather than a straight average, it's based on the number of reads
# apply weighting
z[to.weight] <- lapply(z[to.weight], function(zz) {
if (length(weighting) == length(zz)) {
sum(weighting * zz)
} else {
NaN
}
})
# quickly fix character variables
char <- which(sapply(z, length) > 1 & sapply(z, class) == "character")
z[char] <- lapply(z[char], function(zz) {
paste(zz, collapse = " & ")
})
return(z)
})
w <- lapply(y, as.data.frame)
w <- do.call("rbind", w)
comp <- as.data.frame(pd)
comp <- comp[,57:64]
m <- match(rownames(comp), rownames(w)) # they are
md<- cbind(w, comp[m,])
colnames(md) = bseq.annot$BITColumnName[match(colnames(md), bseq.annot$OriginalMetadataColumnName)]
# Adding features
md %<>%
rownames_to_column("SampleID") %>%
mutate(Period = ifelse(.$AgeNumeric > 0, "Postnatal", "Prenatal"),
StructureAcronym = gsub("HIPPO", "HIP", .$StructureAcronym),
Diagnosis = gsub("Schizo", "Schizophrenia", .$Diagnosis)) %>%
mutate(Regions = add_feature(.$StructureAcronym, regions)) %>%
mutate(Age_rounded = as.character(sapply(na.omit(.$AgeNumeric), num_to_round))) %>% as.data.frame() %>%
mutate(AgeInterval = as.character(add_feature(.$Age_rounded, age_intervals))) %>%
dplyr::select(-Age_rounded) %>%
dplyr::select("SampleID", everything()) %>%
as.data.frame()
md %>%
DT::datatable(caption = "BrainSeq Metadata after harmonizing column names, ages and structure acronyms")
exp = rse_gene@assays@.xData$data$rpkm
rownames(exp) <- sub("\\.[0-9]*$", "", rownames(exp))
write.csv(exp, file = file.path(bseq.out, "BrainSeq-exp.csv"))
write.csv(md, file = file.path(bseq.out, "BrainSeq-metadata.csv"))
write.csv(md, file = file.path(here::here("data/metadata"), "BrainSeq-metadata.csv"))
bspan.annot = read.csv(here::here("data/annotations/BrainSpan-metadata-annot.csv"), header = TRUE)
columns.metadata = read.csv(file.path(bspan, "columns_metadata.csv"), header = TRUE)
exp = read.csv(file.path(bspan, "expression_matrix.csv"), header= FALSE, row.names= 1)
rows.metadata = read.csv(file.path(bspan, "rows_metadata.csv"))
colnames(columns.metadata) = bspan.annot$BITColumnName[match(colnames(columns.metadata),bspan.annot$OriginalMetadataColumnName)]
md = columns.metadata %>%
mutate(Stage = add_feature(.$Age, stages),
AgeInterval = add_feature(.$Age, age_intervals),
Diagnosis = "Control",
Age = gsub(" ","_", .$Age)) %>%
mutate(SampleID = paste(DonorID, Age, StructureAcronym, Stage, sep = "_"),
age_for_mRIN = gsub("_", "", .$Age),
DonorName = gsub("\\.","_", .$DonorName),
StructureAcronym = gsub("DFC", "DLPFC", StructureAcronym),
StructureAcronym = gsub("MFC", "ACC", StructureAcronym)) %>%
mutate(Regions = add_feature(.$StructureAcronym, regions)) %>%
mutate('sample name' = paste(DonorName, age_for_mRIN,Sex ,StructureAcronym, sep = "//")) %>%
dplyr::select("SampleID", everything())
md$AgeNumeric[grepl("pcw", md$Age, ignore.case = TRUE)]<-
md$Age[grepl("pcw", md$Age)] %>% str_remove("_pcw")%>%
as.numeric() %>% `-` (40) %>% divide_by(52)
md$AgeNumeric[grepl("mos", md$Age, ignore.case = TRUE)] <-
md$Age[grepl("_mos", md$Age)] %>% str_remove("_mos") %>%
as.numeric() %>% divide_by(12)
md$AgeNumeric[grepl("yrs", md$Age, ignore.case = TRUE)] <-
md$Age[grepl("_yrs", md$Age)] %>% str_remove("_yrs") %>%
as.numeric
md %<>% mutate(Period = ifelse(.$AgeNumeric >= 0, "Postnatal", "Prenatal"),
colname = paste(DonorName, Age, StructureAcronym, sep = "_"))
colnames(exp) = md$SampleID
rownames(exp) <- rows.metadata$ensembl_gene_id
md.excel = read_excel(here::here("data/annotations/BrainSpan-additional.xlsx"),sheet =2, col_names = TRUE, skip =1) %>%
as.data.frame() %>% mutate_at(.vars = "AllenInstituteID",
.funs = gsub, pattern = "\\.", replacement = "\\_") %>%
mutate_at(.vars = "Age", .funs = gsub, pattern = "PCW", replacement = "_pcw") %>%
mutate_at(.vars = "Age", .funs = gsub, pattern = "M", replacement = "_mos") %>%
mutate_at(.vars = "Age", .funs = gsub, pattern = "Y", replacement = "_yrs") %>%
mutate_at(.vars = "Region/Area", .funs = gsub, pattern = "\\/", replacement = "-") %>%
mutate(colname = paste(AllenInstituteID, Age, `Region/Area`, sep = "_")) %>%
dplyr::select(-c(Agerange, Age, Description))
md = md %>%
left_join(.,md.excel, by = "colname", keep = TRUE)
md.excel = read_excel(here::here("data/annotations/BrainSpan-additional.xlsx"), sheet = 1, col_names = TRUE) %>%
as.data.frame() %>%
mutate_at(.vars="Internal ID", .funs = gsub, pattern = "\\.", replacement = "\\_") %>%
dplyr::rename("Braincode" = "External ID")
md %<>%
left_join(md.excel, by ="Braincode") %>%
dplyr::select(-c("Age.y", "colname.y", "colname.x", "Gender", AllenInstituteID,
"Region/Area", "age_for_mRIN", "sample name", "Internal ID")) %>%
dplyr::rename("Ethnicity"="Ethn.") %>%
distinct(SampleID, .keep_all = TRUE)
md = md[!duplicated(md[,c('column_num')]),]
md %<>%
dplyr::arrange(column_num)
md %>%
DT::datatable(caption = "BrainSpan metadata after metadata harmonization")
write.csv(exp, file = file.path(bspan.out, "BrainSpan-exp.csv"))
write.csv(md, file = file.path(bspan.out, "BrainSpan-metadata.csv"))
write.csv(md, file = file.path(here::here("data/metadata"), "BrainSpan-metadata.csv"))
gtex.annot = read.csv(here::here("data/annotations/GTEx-metadata-annot.csv"), header = TRUE)
attributes = list.files(gtex, full.names = TRUE, pattern = "\\SampleAttributesDS.txt") # Sample attributes contains sample level information
phenotype = list.files(gtex, full.names = TRUE, pattern = "\\SubjectPhenotypesDS.txt") # Phenotype level information related to each donor
exp = list.files(gtex, full.names = TRUE, pattern = "\\gene_tpm.gct.gz") # File used for expression matrix
md.attrbutes = read_tsv(attributes, col_types = c('.default' = 'c')) %>%
dplyr::filter(SMTS == 'Brain') %>%
mutate(SUBJID = sapply(str_split(SAMPID, pattern = "-"),
function(x) paste(x[1:2], collapse = '-'))) %>%
left_join(read_tsv(phenotype, col_types = c('.default' = 'c'))) %>%
as.data.frame()
colnames(md.attrbutes) = gtex.annot$BITColumnName[match(colnames(md.attrbutes), gtex.annot$OriginalMetadataColumnName)]
md.attrbutes %<>%
as.data.frame() %>%
mutate(StructureAcronym = add_feature(.$StructureAcronym, structure_acronym)) %>%
mutate(Regions = add_feature(.$StructureAcronym, regions),
AgeInterval = paste(.$AgeInterval, "yrs", sep = ""),
Diagnosis = "Control",
Sex = ifelse(Sex == 1, "M", "F"),
Period = "Postnatal") %>% as.data.frame()
exp = read.delim(exp, skip = 2)
colnames(exp) <- gsub("\\.", "-", colnames(exp)) # Changing expression file names to match metadata SampleIDs
exp %<>% column_to_rownames("Name")
exp = exp %>%
dplyr::select(contains(md.attrbutes$SampleID))
message(paste0("Samples subsetted - Exp matrix contains ", ncol(exp), " samples"))
rownames(exp) <- sub("\\.[0-9]*$", "", rownames(exp))
md.attrbutes= md.attrbutes[which(md.attrbutes$SampleID %in% colnames(exp)),]
md.attrbutes %>%
DT::datatable(caption="GTEx metadata after preprocessing")
write.csv(exp, file = file.path(gtex.out, "GTEx-exp.csv"))
write.csv(md.attrbutes, file = file.path(gtex.out, "GTEx-metadata.csv"))
write.csv(md.attrbutes, file = file.path(here::here("data/metadata"), "GTEx-metadata.csv"))
Recount3 contains over 70,000 uniformly processed human RNA-seq
samples. Recount provides gene, exon and exon-exon junction count
matrices both in text format and as a
RangedSummarizedExperiment.
The reads from recount were algined with the splice-aware Rail-RNA aligner. To compute the gene count matrices, the mapped reads were quantified with Gencode v25 with hg38 coordinates.
Unlike traditional quantification methods, recount3 provides base-pair coverage counts. Essentially, these are created in the following manner:
hdbr = recount3::create_rse_manual(
project = "ERP016243",
type = "gene"
)
hdbr_annot = read.csv(here::here("data/annotations/HDBR-metadata-annot.csv"), header=TRUE)
hdbr_supp = read_excel("/home/neuro/Documents/BrainData/Bulk/HDBR/Amended Supplementary Table 1.xlsx",
sheet = 1, skip =6) %>%
as.data.frame()
colnames(hdbr_supp) = c("DonorID","Age", "SampleID", "Structure","Hemisphere", "Sex", "PMI")
hdbr_supp %<>% dplyr::select("DonorID","Structure", "PMI")
hdbr_phenotype = read.csv("/home/neuro/Documents/BrainData/Bulk/HDBR/hdbr-phenotype.csv",
header=TRUE)
load(here::here("data/annotations/HDBR.Rda"))
md.hdbr = md.hdbr[,1:15]
colnames(md.hdbr) = hdbr_annot$BITColumnName[match(colnames(md.hdbr), hdbr_annot$OriginalMetadataColumnName)]
md.hdbr %<>%
as.data.frame() %>%
dplyr::select(-c(Block, OntologyIndividual, KaryotypeOntology, Organism, OrganismOntology,
StructureOntology, HemisphereOntology, OntologyAge)) %>%
mutate(AgeInterval = add_feature(.$Age, age_intervals)) %>%
dplyr::rename("Structure"= "StructureAcronym") %>%
left_join(strc_acr, by = "Structure") %>%
left_join(rgns_ftl, by = "StructureAcronym") %>%
mutate(Sex = toupper(Sex)) %>%
mutate(Sex = ifelse(str_detect(Sex,"XX"), "F",
ifelse(str_detect(Sex, "XY"), "M", "Unknown" )),
Diagnosis = c("Control"),
Period = c("Prenatal"))
md = colData(hdbr) %>%
as.data.frame() %>%
dplyr::select(-contains("2"))
colnames(md) = hdbr_annot$BITColumnName[match(colnames(md), hdbr_annot$OriginalMetadataColumnName)]
md.hdbr %<>%
mutate(Hemisphere = gsub("right", "R", Hemisphere),
Hemisphere = gsub("left", "L", Hemisphere),
Hemisphere = gsub("frontal", "Frontal", Hemisphere))
hdbr_supp %<>%
distinct(DonorID, .keep_all = TRUE) %>%
mutate(PMI = ifelse(PMI == "UNKNOWN", NA, PMI))
md.hdbr %<>%
left_join(hdbr_supp, by = c("DonorID"))
full.md.hdbr =left_join(md.hdbr, md)
full.md.hdbr %<>%
mutate(PMI = as.numeric(PMI))
full.md.hdbr %<>%
dplyr::filter(Structure.x != "stomach")
assay(hdbr, "counts") = transform_counts(hdbr, round = TRUE)
hdbr.tpm = recount::getTPM(hdbr)
filter <- rowSums(hdbr.tpm, na.rm = TRUE) > 0.05
hdbr.filter.tpm = hdbr.tpm[filter,]
hdbr.filter.tpm <- thresh(hdbr.filter.tpm)
hdbr.final = hdbr.tpm %>%
as.data.frame() %>%
dplyr::select(contains(full.md.hdbr$SampleID)) %>%
rownames_to_column("EnsemblID") %>%
mutate_at(.vars = "EnsemblID", .funs = gsub, pattern = "\\.[0-9]*$", replacement = "")
write.csv(hdbr.final, file =file.path(hdbr.out, "HDBR-exp.csv"))
write.csv(full.md.hdbr, file =file.path(hdbr.out, "HDBR-metadata.csv"))
write.csv(full.md.hdbr, file = file.path(here::here("data/metadata"), "HDBR-metadata.csv"))
full.md.hdbr %>%
DT::datatable(caption="HDBR metadata after harmonization")
pe.annot = read.csv(here::here("data/annotations/PsychEncode-metadata-annot.csv"), header=TRUE)
exp = list.files(psych, full.names = TRUE, pattern = "\\Gene_expression_matrix_TPM.txt") %>%
read.table(., header=TRUE, row.names = 1, check.names = FALSE)
md = list.files(psych, full.names = TRUE, pattern = "Job*") %>% read.csv(., header=TRUE) %>%
dplyr::filter(individualID != "2015-1")
md.clinical = read.csv(file.path(psych, "PEC_capstone_data_map_clinical.csv"), header=TRUE, check.names = FALSE)
comp = list.files(psych, full.names = TRUE, pattern = "\\Cell_fractions*") %>% read_excel() %>%
as.data.frame() %>%
column_to_rownames("CellType")
colnames(md) = pe.annot$BITColumnName[match(colnames(md), pe.annot$OriginalColumnName)]
# Fix existing columns
comp = comp[,-1]
comp = as.data.frame(t(comp))
m <- match(md$SampleID, rownames(comp))
md <- cbind(md, comp[m,])
# PCW to age numeric
md$AgeNumeric[grepl("PCW", md$AgeNumeric, ignore.case = TRUE)] = md$AgeNumeric[grepl("PCW", md$AgeNumeric)] %>%
str_remove("PCW")%>%
as.numeric() %>% `-` (40) %>% divide_by(52)
md$AgeNumeric = gsub("90+", "91", md$AgeNumeric)
md$AgeNumeric =gsub("\\+", "", md$AgeNumeric)
md$AgeNumeric <- as.numeric(as.character(md$AgeNumeric))
md %<>%
dplyr::filter(Diagnosis == "Affective Disorder" |
Diagnosis == "Autism Spectrum Disorder" |
Diagnosis == "Bipolar Disorder" |
Diagnosis == "Control" |
Diagnosis == "Schizophrenia") %>%
mutate(Structure = c("Dorsolateral Prefrontal Cortex"), ## Adding name of structure
StructureAcronym = c("DLPFC")) %>%
mutate(Period = ifelse(.$AgeNumeric >= 0, "Postnatal", "Prenatal")) %>%
mutate(Age_rounded = as.character(sapply(.$AgeNumeric, num_to_round))) %>% as.data.frame() %>%
mutate(AgeInterval = as.character(add_feature(.$Age_rounded, age_intervals)),
Death = as.character(add_feature(.$causeDeath, death_cause))) %>%
mutate(Regions = c("Cortex")) %>%
mutate(DonorID = as.character(.$SampleID)) %>%
dplyr::select(-Age_rounded) %>%
as.data.frame()
exp = exp %>%
dplyr::select(contains(md$SampleID))
md = md %>%
dplyr::filter(SampleID %in% colnames(exp))
exp = exp[colnames(exp) %in% md$SampleID,]
md = md[md$SampleID %in% colnames(exp),]
write.csv(exp, file = file.path(pe.out, "PsychEncode-exp.csv"))
write.csv(md, file = file.path(pe.out,"PsychEncode-metadata.csv"))
write.csv(md, file = file.path(here::here("data/metadata"), "PsychEncode-metadata.csv"))
rm(md)
rm(exp)
summarise_stats = function(x, dataset)
{
age = table(x$AgeInterval) %>% melt()
age$Type = c(paste(dataset, "Sample", sep = "_"))
individuals = x %>% group_by(AgeInterval, DonorID) %>%
dplyr::summarise(n = n()) %>%
as.data.frame()
individuals = table(individuals$AgeInterval) %>% melt()
individuals$Type = c(paste("Individual", dataset, sep = "_"))
age = rbind(age, individuals)
colnames(age) = c("AgeInterval", "n", "Type")
age$dataset = c(as.character(dataset))
return(age)
}
summarise_regions = function(x, dataset){
regions = x %>%
dplyr::group_by(AgeInterval, Regions) %>%
dplyr::summarise(n = n()) %>%
as.data.frame() %>%
mutate(Dataset = dataset)
return(regions)
}
age_interval_stats = list()
directory = file.path("/home/neuro/Documents/Brain_integrative_transcriptome/BITHub-preprocessing/data/metadata")
pattern = "/home/neuro/Documents/Brain_integrative_transcriptome/BITHub-preprocessing/data/metadata/"
for (f in directory){
md = list.files(f, full.names = TRUE, pattern = "\\-metadata.csv$")
for (j in md){
ct_file = read.csv(j, header= TRUE)
dataset = gsub(pattern, "", j)
dataset = gsub("\\-metadata.csv","", dataset)
message("Now calculating statistics for ", dataset)
stats = summarise_stats(ct_file, dataset)
age_interval_stats[[paste(dataset)]] = stats
}
}
bulk_plot_version2 = age_interval_stats %>%
do.call(rbind, .) %>%
mutate(AgeInterval = factor(AgeInterval, levels = c("4-7pcw", "8-9pcw",
"10-12pcw", "13-15pcw", "16-18pcw",
"19-24pcw", "25-38pcw", "0-5mos",
"6-18mos", "19mos-5yrs", "6-11yrs",
"12-19yrs", "20-29yrs", "30-39yrs", "40-49yrs",
"50-59yrs", "60-69yrs", "70-79yrs", "80-89yrs", "90-99yrs")),
Type = factor(Type, levels = c("BrainSeq_Sample", "Individual_BrainSeq",
"BrainSpan_Sample", "Individual_BrainSpan",
"GTEx_Sample", "Individual_GTEx",
"HDBR_Sample", "Individual_HDBR",
"Ramakar_Sample", "Individual_Ramakar",
"PsychEncode_Sample", "Individual_PsychEncode"))) %>%
drop_na() %>%
ggplot(aes(x= AgeInterval, y = n, fill =Type)) +
geom_bar(position = "dodge",stat= "identity") +
facet_grid(dataset~AgeInterval, scales = "free") + xlab("") + ylab("") + theme_bw() +
theme(legend.position = "none", axis.text.x=element_blank(),
strip.background =element_rect(fill="#AA9A9C", color = "#E1DFDB"),
panel.border = element_rect(color = "#f7f4ed", fill = NA, size = 2)) +
theme(strip.text = element_text(colour = 'white')) +
scale_fill_manual(values = c("#F75E5E", "#FFC6BD",
"#49165E", "#EBBAFF",
"#78A2EB", "#36466A",
"#74C69D", "#2D6A4F",
"#F9AD79", "#FF5F0F")) +
theme(
panel.background = element_rect(fill = "transparent",colour = NA), # or theme_blank()
plot.background = element_rect(fill = "transparent",colour =NA)
)
bulk_plot_version2

#ggsave(width = 11.01, height = 9.30, units = "in",
# file = "../../Results/exploratory/bulk_dist_thesis.svg", plot = bulk_plot_version2)
#ggsave(file = "../../Results/exploratory/bulk_dist_update.svg", bulk_plot,
# height = 18.9624, width = 45.3501, units = "cm")
sessionInfo()
R version 4.3.3 (2024-02-29)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
time zone: Australia/Adelaide
tzcode source: system (glibc)
attached base packages:
[1] tools grid stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] lubridate_1.9.3 forcats_1.0.0
[3] purrr_1.0.2 readr_2.1.5
[5] tidyverse_2.0.0 stringr_1.5.1
[7] tidyr_1.3.0 scales_1.3.0
[9] data.table_1.14.10 readxl_1.4.3
[11] naniar_1.0.0 glmpca_0.2.0
[13] broom_1.0.5 cowplot_1.1.2
[15] glue_1.7.0 ggfortify_0.4.16
[17] stargazer_5.2.3 AnnotationHub_3.10.0
[19] BiocFileCache_2.10.1 dbplyr_2.4.0
[21] singscore_1.22.0 EnsDb.Hsapiens.v86_2.99.0
[23] ensembldb_2.26.0 AnnotationFilter_1.26.0
[25] GenomicFeatures_1.54.1 AnnotationDbi_1.64.1
[27] DT_0.31 variancePartition_1.32.2
[29] BiocParallel_1.36.0 limma_3.58.1
[31] gridExtra_2.3 pander_0.6.5
[33] ggvenn_0.1.10 ggplot2_3.4.4
[35] dplyr_1.1.4 corrplot_0.92
[37] reshape2_1.4.4 tibble_3.2.1
[39] magrittr_2.0.3 recount3_1.12.0
[41] SummarizedExperiment_1.32.0 Biobase_2.62.0
[43] GenomicRanges_1.54.1 GenomeInfoDb_1.38.5
[45] IRanges_2.36.0 S4Vectors_0.40.2
[47] BiocGenerics_0.48.1 MatrixGenerics_1.14.0
[49] matrixStats_1.2.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.3 ProtGenerics_1.34.0
[3] bitops_1.0-7 httr_1.4.7
[5] numDeriv_2016.8-1.1 doRNG_1.8.6
[7] backports_1.4.1 utf8_1.2.4
[9] R6_2.5.1 lazyeval_0.2.2
[11] withr_3.0.0 prettyunits_1.2.0
[13] cli_3.6.2 labeling_0.4.3
[15] sass_0.4.8 mvtnorm_1.2-4
[17] recount_1.28.0 Rsamtools_2.18.0
[19] foreign_0.8-86 R.utils_2.12.3
[21] rentrez_1.2.3 sessioninfo_1.2.2
[23] BSgenome_1.70.1 rstudioapi_0.15.0
[25] RSQLite_2.3.5 generics_0.1.3
[27] BiocIO_1.12.0 gtools_3.9.5
[29] crosstalk_1.2.1 vroom_1.6.5
[31] Matrix_1.6-5 fansi_1.0.6
[33] abind_1.4-5 R.methodsS3_1.8.2
[35] lifecycle_1.0.4 whisker_0.4.1
[37] yaml_2.3.8 edgeR_4.0.11
[39] qvalue_2.34.0 gplots_3.1.3
[41] SparseArray_1.2.3 blob_1.2.4
[43] promises_1.2.1 crayon_1.5.2
[45] lattice_0.22-5 annotate_1.80.0
[47] KEGGREST_1.42.0 pillar_1.9.0
[49] knitr_1.45 rjson_0.2.21
[51] boot_1.3-28.1 corpcor_1.6.10
[53] codetools_0.2-19 getPass_0.2-4
[55] downloader_0.4 vctrs_0.6.5
[57] png_0.1-8 Rdpack_2.6
[59] cellranger_1.1.0 gtable_0.3.4
[61] cachem_1.0.8 xfun_0.41
[63] rbibutils_2.2.16 S4Arrays_1.2.0
[65] mime_0.12 iterators_1.0.14
[67] statmod_1.5.0 interactiveDisplayBase_1.40.0
[69] ellipsis_0.3.2 nlme_3.1-164
[71] pbkrtest_0.5.2 bit64_4.0.5
[73] progress_1.2.3 EnvStats_2.8.1
[75] filelock_1.0.3 rprojroot_2.0.4
[77] GenomicFiles_1.38.0 bslib_0.6.1
[79] rpart_4.1.23 KernSmooth_2.23-22
[81] Hmisc_5.1-1 colorspace_2.1-0
[83] DBI_1.2.1 nnet_7.3-19
[85] tidyselect_1.2.0 processx_3.8.3
[87] bit_4.0.5 compiler_4.3.3
[89] curl_5.2.0 git2r_0.33.0
[91] graph_1.80.0 htmlTable_2.4.2
[93] derfinder_1.36.0 xml2_1.3.6
[95] DelayedArray_0.28.0 rtracklayer_1.62.0
[97] checkmate_2.3.1 caTools_1.18.2
[99] remaCor_0.0.16 callr_3.7.3
[101] rappdirs_0.3.3 digest_0.6.34
[103] minqa_1.2.6 rmarkdown_2.25
[105] GEOquery_2.70.0 aod_1.3.3
[107] XVector_0.42.0 RhpcBLASctl_0.23-42
[109] base64enc_0.1-3 htmltools_0.5.7
[111] pkgconfig_2.0.3 lme4_1.1-35.1
[113] highr_0.10 fastmap_1.1.1
[115] rlang_1.1.3 htmlwidgets_1.6.4
[117] shiny_1.8.0 farver_2.1.1
[119] jquerylib_0.1.4 jsonlite_1.8.8
[121] R.oo_1.25.0 VariantAnnotation_1.48.1
[123] RCurl_1.98-1.14 Formula_1.2-5
[125] GenomeInfoDbData_1.2.11 munsell_0.5.0
[127] Rcpp_1.0.12 visdat_0.6.0
[129] stringi_1.8.3 zlibbioc_1.48.0
[131] MASS_7.3-60.0.1 bumphunter_1.44.0
[133] plyr_1.8.9 parallel_4.3.3
[135] Biostrings_2.70.1 splines_4.3.3
[137] hms_1.1.3 derfinderHelper_1.36.0
[139] locfit_1.5-9.8 ps_1.7.6
[141] rngtools_1.5.2 biomaRt_2.58.0
[143] BiocVersion_3.18.1 XML_3.99-0.16
[145] evaluate_0.23 BiocManager_1.30.22
[147] foreach_1.5.2 nloptr_2.0.3
[149] tzdb_0.4.0 httpuv_1.6.13
[151] xtable_1.8-4 restfulr_0.0.15
[153] fANCOVA_0.6-1 later_1.3.2
[155] lmerTest_3.1-3 memoise_2.0.1
[157] GenomicAlignments_1.38.2 cluster_2.1.6
[159] timechange_0.3.0 GSEABase_1.64.0
[161] here_1.0.1